Big Data

Apache Spark

Overview

When your data outgrows a single machine, we turn to Apache Spark. Our engineers build distributed data processing pipelines that handle terabytes to petabytes of data — using Spark SQL for analytics, MLlib for machine learning, and Structured Streaming for real-time workloads.

Our Capabilities

1.Distributed data processing at scale

2.Spark SQL for interactive analytics

3.MLlib for large-scale machine learning

4.Structured Streaming for real-time data

5.Delta Lake for reliable data lakes

6.PySpark & Scala API development

7.Cluster optimization & performance tuning

8.Integration with Kafka, S3, HDFS & more

Common Use Cases

1.Petabyte-scale ETL pipelines

2.Real-time fraud detection

3.Log analytics & anomaly detection

4.Large-scale feature engineering

Want to leverage Apache Spark for your project?

Let's discuss how we can use Apache Spark to solve your specific data challenges.

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